import pandas as pd 
import plotly.express as px  # For high-level API
import plotly.graph_objects as go  # For low-level API
import plotly.io as pio  # For handling input/output operations like saving figures

data = pd.read_csv("customer_acquisition_data.csv")

data.head(5)

	customer_id	channel	cost	conversion_rate	revenue
0	1	referral	8.320327	0.123145	4199
1	2	paid advertising	30.450327	0.016341	3410
2	3	email marketing	5.246263	0.043822	3164
3	4	social media	9.546326	0.167592	1520
4	5	referral	8.320327	0.123145	2419

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 800 entries, 0 to 799
Data columns (total 5 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   customer_id      800 non-null    int64  
 1   channel          800 non-null    object 
 2   cost             800 non-null    float64
 3   conversion_rate  800 non-null    float64
 4   revenue          800 non-null    int64  
dtypes: float64(2), int64(2), object(1)
memory usage: 31.4+ KB

fig = px.histogram(data,x = "cost",
                   nbins = 20 ,
                   title = "Distribution of Acquistion cost")
fig.show()

import plotly.express as px

fig = px.histogram(data, x="revenue", 
                   title="Distribution of Revenue Generated by Customers",
                   nbins=30,  
                   marginal="box",
                   color_discrete_sequence=["purple"])

fig.show()

cost_by_channel = data.groupby('channel')['cost'].mean().reset_index()

fig = px.bar(cost_by_channel, x="channel", y="cost", 
             title="Average Customer Acquisition Cost by Channel",
             labels={"cost": "Avg Acquisition Cost", "channel": "Acquisition Channel"}
            )

fig.show()

cost_by_channel 

	channel	cost
0	email marketing	5.246263
1	paid advertising	30.450327
2	referral	8.320327
3	social media	9.546326

conversion_by_channel = data.groupby('channel')['conversion_rate'].mean().reset_index()

conversion_by_channel

	channel	conversion_rate
0	email marketing	0.043822
1	paid advertising	0.016341
2	referral	0.123145
3	social media	0.167592

fig = px.bar(conversion_by_channel, x="channel", y="conversion_rate",
             title="Average Conversion Rate by Acquisition Channel",
             labels={"conversion_rate": "Avg Conversion Rate", "channel": "Acquisition Channel"},
             color="conversion_rate",
             color_continuous_scale="viridis")

fig.show()

Revenue_by_channel = data.groupby('channel')['revenue'].sum().reset_index()

fig = px.pie(Revenue_by_channel,values = 'revenue',names = 'channel',title = 'Total Revenue by Channel', hole = 0.5,
             color_discrete_sequence=px.colors.qualitative.Pastel)
fig.show()

Revenue_by_channel

	channel	revenue
0	email marketing	604706
1	paid advertising	548396
2	referral	569552
3	social media	492667

data['roi'] = data['revenue']/data['cost']
roi_by_channel = data.groupby('channel')['roi'].mean().reset_index()

fig = px.bar(roi_by_channel,x = "channel", y = "roi", 
             title = "Return on investment [roi] by channel")
fig.show()